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WO2009029810A1 - Sélection de temps d'exposition automatique pour un tissu d'imagerie - Google Patents

Sélection de temps d'exposition automatique pour un tissu d'imagerie Download PDF

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Publication number
WO2009029810A1
WO2009029810A1 PCT/US2008/074817 US2008074817W WO2009029810A1 WO 2009029810 A1 WO2009029810 A1 WO 2009029810A1 US 2008074817 W US2008074817 W US 2008074817W WO 2009029810 A1 WO2009029810 A1 WO 2009029810A1
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Prior art keywords
image
exposure time
pixel
frequency distribution
dynamic range
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Inventor
Dylan M. Reilly
Brian Bourke-Martin
Mark Gustavson
Jason Christiansen
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HistoRx Inc
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HistoRx Inc
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/70—Circuitry for compensating brightness variation in the scene
    • H04N23/73—Circuitry for compensating brightness variation in the scene by influencing the exposure time

Definitions

  • the present invention relates generally to the field of imaging of biological specimens i.e., histology specimens such as tissue micro array (TMA) "histospots" and whole tissue section (WTS) fields of view (FOV) for image analysis.
  • biological specimens i.e., histology specimens such as tissue micro array (TMA) "histospots" and whole tissue section (WTS) fields of view (FOV) for image analysis.
  • TMA tissue micro array
  • WTS whole tissue section
  • FOV fields of view
  • the operator may select what appears to be the highest expressing fields of view of a whole tissue section, or the highest expressing histospots of a TMA to determine exposure times that are then applied to all other images acquired during a particular test.
  • Such an approach generally limits the overall dynamic range of an assay in that optimization of image acquisition for an expression level of a single sample may be to the detriment of the other samples.
  • different users may determine different initial exposure times for application across samples in a particular study or different fields of view or different histospots. In either instance, such operator- dependant variability introduces possible variations even when using the same system.
  • first operator interaction time related to examination of the sample and determination of a field of view (histospots) and a second operator interaction time related to exposure time selection and performance of the actual setup.
  • the systems and processes described herein provide automatic selection of the appropriate exposure times for image acquisition using a microscope system. Such automated selection can be repeated for all channels or fluorescent signals being monitored in a fluorescence microscope based System. Alternatively or in addition, such automated selection can be repeated for all fields being examined. In some embodiments, every field of view (or histospot) examined, the camera exposure is optimized before each image is acquired with these exposure times re-optimized for each subsequent image. This procedure results in a high degree of correlation between data acquired using the processes of the invention with data acquired by a highly trained operator in manual mode.
  • the invention relates to a process for automatically optimizing a dynamic range of pixel intensity data obtained from a digital image.
  • the process includes capturing an image of a subject within the field of view of a camera at a first exposure time, resulting in a captured image comprising a predetermined number of pixels, wherein each pixel has an intensity value.
  • a frequency distribution of pixel intensities of the captured image is queried to determine a region of the greatest frequency occurrence of the pixel intensities of the frequency distribution. Exposure time is then adjusted from the first exposure time to shift that region of highest frequency distribution toward the middle of the range of intensity values.
  • the center of mass of a histogram, or frequency distribution is determined from which an adjusted exposure time is calculated to achieve an optimized dynamic range of pixel intensities.
  • a second image of the subject can then be captured at the adjusted exposure time resulting in an image having an optimal dynamic range.
  • a process for automatically improving a dynamic range of a digital image includes: (a) capturing a first image of a subject within the field of view of a camera at a first exposure time, resulting in a captured first image comprising a predetermined number of pixels, each pixel having an intensity value, (b) evaluating a frequency distribution of the pixel intensity values of the first image; (c) determining a region of highest frequency distribution of the histogram to determine an exposure time adjustment to shift the region of highest frequency distribution toward a center of a range of pixel intensities; in order to calculate an adjusted exposure time; and (d) capturing a second image of the subject at the adjusted exposure time thereby obtaining an image with an improved dynamic range.
  • the dynamic range is automatically improved to an optimal dynamic range.
  • the process further comprises ensuring that no more than a predetermined threshold number of the image pixel intensity values are saturated.
  • the predetermined threshold number of image pixel intensity values which is found to be saturated, falls in the range of about 0.0% to about 0.05%. Ideally no more than about 0.02% of the image pixel intensity values are saturated.
  • step (c), above further comprises determining the adjusted exposure time as a function of the center of mass and a midpoint of the histogram.
  • the adjusted exposure time provides an image in which the center of mass of the histogram coincides with the midpoint of the histogram.
  • the process of the invention includes iteratively conducting the steps (a) - (d), above, to provide an image having an improved or optimal dynamic range. For example, conducting the steps iteratively ceases when the difference between the exposure time and the adjusted exposure time is less than a predetermined tolerance.
  • the number of iterations does not exceed 150 iterations, more preferably, the number of iterations does not exceed 30 iterations.
  • the predetermined tolerance is determined by the equation:
  • the disclosed process further comprises flagging or otherwise identifying an image if after iteratively improving the dynamic range, the center of mass does not coincide with the midpoint of the histogram. What is more, such flagging of the image can be used to remove it from a dataset used for further analytical evaluation.
  • a preferred process may further comprise adjusting the exposure time to reduce the number of image pixel intensity values that are saturated by capturing a new image at one half the first exposure time.
  • the exposure time is adjusted iteratively until the center of mass corresponds to the midpoint of the histogram.
  • a process is described in which the exposure time is adjusted to reduce the number of image pixel intensity values that are saturated by capturing a new image at a new exposure time that is proportionally lower than the first exposure time based on the number of saturated pixel intensity values.
  • the exposure time can be adjusted iteratively until the center of mass corresponds to the midpoint of the histogram.
  • the exposure time can also be adjusted to reduce the number of image pixel intensity values that are saturated by capturing a new image at a reduced, or minimum exposure time in the step of capturing an image.
  • a preferred subject includes a biological specimen, a biological tissue specimen, a whole tissue section, a tissue microarray, or combinations thereof.
  • the subject may further include a tissue section, preferably one that has been stained, more preferably with a fluorescent substance.
  • an image is captured by the camera through a microscope.
  • the process disclosed may also be applied to capturing a plurality of such images, such as tissue "histospots" contained in a tissue microarray.
  • a process of normalizing quantitative data across multiple slides, instruments and operators includes: (a) capturing a first image from a first sample at a first exposure time, resulting in a captured first image comprising a predetermined number of pixels each pixel having an intensity value; (b) querying a first frequency distribution of pixel intensities values of the captured first image; to determine a center of mass of the first frequency distribution to determine a second, adjusted exposure time; (c) capturing a second image of the first sample at the second, adjusted exposure time thereby improving the dynamic range of the second image over the first image; (d) capturing a third image from a second sample at a third exposure time, resulting in a captured third image comprising a predetermined number of pixels each pixel having an intensity value; (e) querying a second frequency distribution of pixel intensities values of the captured third image to determe a center of mass of the second frequency distribution to determine a fourth, adjusted exposure time to provide a fourth image having a dynamic range
  • a computer-usable medium having computer readable instructions stored thereon for execution by a processor to perform a process for automatically adjusting the exposure time associated with a captured image, wherein the instructions comprise steps performed iteratively of: (a) capturing an image of a subject at a first exposure time, resulting in a captured image comprising a predetermined number of pixels each pixel having an intensity value; (b) querying a frequency distribution of pixel intensity values; (c) determining a center of mass of the frequency distribution of pixel intensity values to determine an adjusted exposure time; and (d) capturing a subsequent image of the subject at the adjusted exposure time thereby obtaining an image with an optimal dynamic range.
  • the invention also encompasses an electromagnetic signal carrying computer- readable instructions stored thereon for execution by a processor to perform a process for automatically adjusting an exposure time to optimize a dynamic range of a digital image includes: (a) capturing an image of a subject at a first exposure time, resulting in a captured image comprising a predetermined number of pixels each pixel having an intensity value; (b) querying a frequency distribution of pixel intensity values of the pixels verses intensity values; (c) determining a center of mass of the frequency distribution of pixel intensity values to determine an adjusted exposure time; and (d) capturing a second image of the subject at the adjusted exposure time thereby obtaining an image with an optimal dynamic range.
  • a system for automatically adjusting an exposure time to improve or optimize a dynamic range of a digital image comprising: a camera or image sensor configured to capture an image of a subject within the field of view at a first exposure time, resulting in a captured image comprising a plurality of pixels, each pixel having an intensity value; a shutter configured to control the exposure time of the camera; and a controller configured to carry out the steps comprising: (a) querying a frequency distribution or histogram of pixel intensity values of the pixels versus intensity values; (b) determining a center of mass of the histogram to determine an adjusted exposure time; and (c) capturing a second image of the subject at the adjusted exposure time thereby obtaining an image with an improved or optimal dynamic range.
  • FIG. 1 illustrates a block diagram of an exemplary imaging microscope system.
  • FIG. 2 illustrates a flow diagram of an embodiment of a procedure embodying the AutoExposure technique.
  • FIG. 3A through FIG. 3B illustrates regression analysis of data generated from images acquired at single exposure time (y axis) and using the AutoExposure techniques described herein (x-axis) for each of two common IHC stains.
  • FIG. 4A and FIG. 4B illustrates clinical outcome data generated from images of
  • FIG. 5A and FIG. 5B illustrates clinical outcome data generated from images of
  • FIG. 6A through FIG. 6D illustrates regression analysis assessing reproducibility of data (AQUA® score) generated from images acquired using the
  • AutoExposure techniques described herein (A), and the reproducibility of AutoExposure techniques described herein dictated exposure time to acquire images in the fluorescent channel for DAPI (B), Cy3 (C) and Cy5(D).
  • FIG. 7 illustrates tabulated results of an exemplary analysis using the
  • an exemplary refiected-light fluorescent microscope system 100 includes a an excitation source 102, an objective lens 104, a sample supporting stage 106, a filter assembly 115 including one or more filters 108', 108", 108'" (generally 108), and an observation head 110.
  • the sample supporting stage 106 is configured to support a sample under test along an optical axis and within a focal plane of the objective lens 104.
  • One of the filters 108 of the filter assembly 115 is also located along the optical axis between the objective lens 104 and the observation head 110.
  • each filter 108 is a three port device with two opposite ports disposed along the optical axis and a third port disposed off-axis. As illustrated, the third port can be orthogonal to a line joining the two opposite ports.
  • Illumination from an excitation source 102 is directed toward the orthogonal port of the filter 108.
  • the filter 108 redirects a portion of the illumination from the excitation source 102 toward the objective lens 104.
  • the objective lens 104 preferably includes a relatively high numerical aperture thereby allowing it to capture a substantial portion of excitation light.
  • the objective lens 104 functions as a condenser directing excitation light toward a sample under test placed upon the stage.
  • multiple objective lenses 104 e.g., 4X, 1OX, 2OX, 4OX, 60X
  • the nosepiece can be manipulated to selectively bring different ones of the multiple objective lenses 104 into alignment with the optical axis to adjust magnification of the sample under test.
  • Sample illumination (emission) from the sample under test travels back along the optical path through the objective lens 104 and into a first one of the opposite ports of the filter 108'. At least a portion of the sample illumination continues along the optical path, exiting a second one of the opposite ports of the filter 108' directed towards the observation head 110.
  • the filter 108' selectively filters the sample illumination passed therethrough. In fluorescence microscopy, filtration can be used to selectively view emissions from different fluorophores (e.g., red, green, blue) used to treat the sample.
  • the microscope system 100 can include multiple filters 108', 108", 108'", each filter 108 tuned to pass a selected wavelength of the sample emission toward the observation head 110.
  • the different filter blocks 108 when housed within a carousel or turret 115, allow for rapid selection of each filter 108 without disturbing the sample under test.
  • the different filters 108 are radially disposed within the turret 115 about an axis of rotation.
  • the turret 115 is positioned with its axis of rotation parallel and to a side of the optical axis, such that one of the filters 108' is aligned with the optical axis. Rotation of the turret 115 selectively moves one filter block 108' out of alignment and brings another one of the filter blocks 108", 108'" into alignment with the optical axis.
  • the observation head 110 directs at least a portion of light from the filter block 108 toward an image collection device, such as a charge coupled device (CCD) camera 112.
  • the observation head 110 additionally includes one or more eyepieces (not shown) allowing for manual observation of the sample under test. Such an eyepiece can be used to adjust placement of a sample 107 upon the stage 106 and to coordinate positioning of the stage 106 before and during test.
  • a first shutter 117 is provided to control exposure time of the sample 107 to the excitation source 102.
  • a second shutter 114 is provided to control exposure time of an imaging device, such as the CCD camera 112.
  • the shutter 114 can be an independent component located along the optical path between the sample under test and the observation head 110. Alternatively or in addition to an independent shutter 114, the shutter can be integrated into the CCD camera 112.
  • the microscope system 100 also includes a controller 116 for controlling the overall image acquisition process.
  • the controller 116 is in communication with one or more sub-elements of the microscope system 110 to allow automated control of the system.
  • the controller 116 is in communication with the excitation source 102, the objective lens 104, the CCD camera 112, the shutter 114, the turret 115, and a stage positional controller 118.
  • the controller 116 can include at least one microprocessor or computer 116 operating under the control of preprogrammed instruction or code.
  • the pre-programmed instructions may be stored on a computer-readable medium, such as a magnetic disk (e.g., a hard disk drive), an electronic memory (e.g., a RAM), or an optical media, such as an optical disk (e.g., a CD ROM).
  • a computer-readable medium such as a magnetic disk (e.g., a hard disk drive), an electronic memory (e.g., a RAM), or an optical media, such as an optical disk (e.g., a CD ROM).
  • the preprogrammed instructions can be configured to implement one or more of the procedural steps referred to herein.
  • the controller 116 sends a signal to the stage positional controller 118 to position the stage 106, such that a selected for or histospot 109 of the sample under test is brought into alignment with the optical axis.
  • the controller 1 16 also sends a signal to an axial translator 119 configured to position and reposition the objective lens 104 along the optical axis with respect to the stage 106 bringing a magnified image of the sample into focus.
  • the controller 116 sends a second signal to the nosepiece causing it to rotate a selected one of multiple objective lenses 104 into alignment with the optical axis prior to focusing.
  • the controller 116 also sends a signal to the turret 115 causing a controlled rotation of the turret to select one of the multiple filters 118. In response, the turret 115 rotates, bringing the selected one of the filters 118 into alignment with the optical axis.
  • the controller 116 next sends a signal to the excitation source 102 turning the source 102 on, at least momentarily, to illuminate the sample under test.
  • the image sensor, or camera shutter 114 is normally closed blocking the optical path between the sample under test and the CCD camera 112.
  • the light source 102 is turned on during initialization of the instrument. With fluorescent microscopes, the high-intensity lamps require a warm-up period to allow intensity of the source 102 to stabilize before any test samples are measured.
  • the light source 102 remains on during operation.
  • the illuminated source or lamp shutter 117 between light source 102 and test sample is used to block illumination of the sample until ready to view the sample and acquire an image of the sample.
  • Such limited exposure of the test sample to illumination may avoid bleaching of the sample.
  • the shutter 117 Upon receiving a first trigger signal from the controller 116, the shutter 117 opens for a predetermined exposure period before closing again.
  • a second trigger signal from the controller is sent to the camera shutter 114. This signal controls exposure thereby allowing for a controlled sampling by the image sensor 112 of emission from the sample under test 107.
  • the first shutter 117 is open for at least the entire duration of an exposure controlled by the second shutter 114.
  • operation of the two shutters 114, 117 can be controlled by a common signal, or otherwise configured to operate in synchronization.
  • the CCD camera 112 captures an electronic image of illumination from the sample under test. The image can be forwarded to the controller 116 or to an external system for analysis.
  • timing of each shutter can be varied to produce different effects.
  • the first shutter 117 is opened to expose test sample for a predetermined period of time and then closed. This can be performed to expose a luminescent test sample to illumination from the source 102.
  • the second shutter 114 could be operated after closure of the first shutter 117 to sample luminescence of the sample, without interference from source illumination.
  • a microscope mounted tissue sample in either tissue micro array (TMA) or whole tissue section (WTS) format treated with immunoassay reagents and detection fluorescent histochemical stains to delineate morphological or quantitative features of the sample (i.e., regions of tumor, sub-cellular compartments and the target biomarker) is analyzed.
  • the stained slide 107 is loaded onto the automated microscope platform 106 and multiple low magnifications images (4X) of a limited field of view are collected to generate a mosaic image of the active region of interest for analysis and determine acquisition parameters for the high resolution acquisitions.
  • TMA tissue micro array
  • WTS whole tissue section
  • a test sample may include a TMA having a matrix of a tissue or cell line samples on a single microscope slide.
  • the test sample may also be a whole tissue section, for example, from a formalin fixed, paraffin embedded sample block.
  • combinations of TMAs and whole tissue sections may be measured on the same slide.
  • a TMA comprised of breast cancer tissue specimens with a range of expression normal, low, medium and high was used as a test sample for the automated exposure processes. Individual sections taken from TMAs (designated 40_7, Yale TMA facility, New Haven, CT) on microscope slides were put through a typical staining procedure as prescribed for quantitative AQUA ® analysis.
  • the staining procedure comprises the following steps. Sections were deparaf ⁇ nized in xylene, rehydrated through a series of decreasing amounts of ethanol to pure water, and subjected to antigen retrieval in Tris EDTA. After endogenous peroxidase blocking and blocking with Background Sniper (Biocare Medical, Concord, CA), primary antibodies to: HER 2, polyclonal (Dako, Carpinteria, CA), Estrogen receptor (ER), Clone 6Fl 1 (Novocastra, Laboratories Ltd, Burlingame, CA) or Progesterone Receptor (PR) Clone PgR636 (Dako) and compartment specific antibody to cytokeratin (Rabbit, Dako) each diluted in Da Vinci Green (Biocare Medical) were applied and rinsed off after one hour.
  • Envision anti-mouse (Dako) and Alexa 555 GAR (Molecular Probes, Eugene OR) were then applied. After extensive washing, TSA Cy 5 tyramide (NEN, Perkin Elmer, Waltham, MA) was applied. The slides were then washed in TBS/Tween 20. Finally, Prolong Gold antifade reagent with DAPI (Molecular Probes) mounting media was applied and the slides were dried. [0039] After slides were stained, digital images for each TMA histospot were acquired on an AQUA ® analysis PM-2000TM system, commercially available from HistoRx, Inc. of New Haven, CT.
  • a fluorescent microscope system is part of an integrated quantitative immunohistochemical (IHC) analysis system, such as the PM- 2000TM system.
  • the IHC analysis system comprises the following components assembled in a light-tight enclosure: a fluorescent microscope, such as the Olympus BX51 epi-fluorescence microscope, commercially available from Olympus America, Inc. of Center Valley, PA; the microscope is equipped with a motorized nosepiece to control selection among different objective lenses (e.g., 4X, 1OX, 2OX, 4OX, 60X), and a motorized filter turret to control selection among different filter cube selection (e.g., in DAPI, Cy2, Cy3, Cy5 and Cy7 or equivalent wavelengths).
  • a fluorescent microscope such as the Olympus BX51 epi-fluorescence microscope, commercially available from Olympus America, Inc. of Center Valley, PA
  • the microscope is equipped with a motorized nosepiece to control selection among different objective lenses (e.g., 4X, 1OX, 2OX
  • the system also includes a motorized stage, such as the Prior Scientific part no. HlOlA.
  • the PCI card that drives the stage is Prior Scientific part no. H252 motorized stage commercially available from Prior Scientific, Inc. of Rockland, MA.
  • the control card occupies a PCE expansion slot within a computer controller. Focus control is facilitated by integrated software.
  • the system also includes a light source, such as the X-CITE 120 system, commercially available from EXFO Life Sciences & Industrial Division of Ontario, Canada, which is equipped with a mercury/metal halide lamp; a monochromatic digital camera for images capture, such as the QU ANTIFIRE camera, commercially available from OPTRONICS of Goleta, California; and a computer controller.
  • the computer is a personal computer running WINDOWS XP or higher operating system environment.
  • each experimental slide or specimen was acquired first using traditional single exposure methods. Each slide was subsequently acquired using AutoExposure techniques described herein. According to a preferred embodiment, the acts of acquiring an image at optimized AutoExposure settings is performed as described in the flow chart shown in FIG. 2.
  • An image is captured at a current exposure time in step 210.
  • the current exposure time can be chosen as a pre-selected for each newly captured image (210). Such a pre-selected value can be a constant for every image. In some embodiments, the constant, pre-selected exposure time is selected to promote overexposure for the first captured image.
  • a distribution of the intensity values for each pixel of the captured image is queried in step 220. In some embodiments, the pixel intensity distribution is a frequency distribution.
  • the frequency distribution can be generated for example as a histogram, in which bins of the histogram (the x-axis) correspond to different intensity values, and the y-axis corresponds to the number, or frequency, of pixels of the captured image counted in each of the different bins.
  • the histogram could be configured to have 256 bins, with each bin corresponding to a distinguishable different pixel intensity ranging from a minim value (i.e., 0) to a maximum value (i.e., 255).
  • a measure of the amount of pixel saturation for example as a ratio of the number of saturated pixels to the number of total pixels, is compared to a threshold saturation value in step 230.
  • the threshold saturation value is selectable, as may be advantageous given the particular application.
  • Saturated pixels can be identified as pixels having a maximum allowable value (e.g., an intensity value of 255 for an 8 bit pixel intensity scale of 0-255). If there are too many saturated pixels, the image may be overexposed. Beneficially, such over exposure can be corrected by reducing the exposure time and re-capturing the image using the reduced exposure time.
  • a reduced exposure time is selected in response to determining that the captured image is overexposed. Such selection of the reduced exposure time can be determined as a function of the amount of over exposure.
  • such selection of the reduced exposure time can be determined according to a selectable aggression value - more aggressive resulting in coarser steps of reduced exposure time, less aggressive resulting in finer steps.
  • the exposure time may be reduced by up to one-half of its current value based on the amount of overexposure in step 240 (this would set an upper limit to the coarseness of steps).
  • Steps 220-230 are then repeated using the new image obtained with the reduced exposure time. If the number of saturated pixels determined in step 230 is less than the predefined threshold saturation value, the image is determined as not being overexposed. If the image is not overexposed and maximization of the dynamic-range, otherwise referred to as the COM analysis herein, has not been executed, then an effective "center of mass" (COM) of the pixel distribution (or histogram when available) is found in step 250. If the COM is sufficiently close to the mid-point or center of the full range of pixel intensities (i.e., the dynamic range) then the image is saved in step 260.
  • COM center of mass
  • a corrected exposure time is determined in step 270.
  • the corrected exposure time is determined as a function of the distance of the COM from the mid-point or center of the possible pixel intensity frequency distribution.
  • AutoExposure may be a process by which the exposure time of the camera imaging tissue micro array (TMA) histospots and whole tissue section (WTS) fields of view (FOV) are adjusted automatically via a software algorithm.
  • TMA camera imaging tissue micro array
  • WTS whole tissue section
  • FOV fields of view
  • One aspect of the algorithm is to set the exposure time as high as possible without overexposing the image. This characteristic will produce images that have a maximized dynamic range with minimal loss of information caused by exposure time.
  • the overall process can include multiple sub-procedures: [0049]
  • a first sub-procedure relates to an overexposure correction procedure. If the current exposure time results in an overexposed image, a reduce exposure time is determined to reduce and in some instances eliminate the amount of overexposure. This sub-procedure can be repeated iteratively until the image is no longer overexposed, or at least until any overexposure is below an acceptable threshold. This overexposure correction procedure yields a sample image exhibiting minimal information loss.
  • a second sub-procedure relates to improving a dynamic range for the tones or pixel intensities of the sample image.
  • This sub-procedure effectively spreads or otherwise adjusts the frequency distribution of pixel intensities of the image over a wider dynamic range by moving a so-called “center of mass” (COM) of the frequency distribution towards a center of the possible range of pixel intensities thereby maximizing the dynamic range for the sample image.
  • COM center of mass
  • the "center of mass” as used herein does not literally refer to usual meaning relating to a weighted average of mass with respect to distance. Rather, the concept of a COM is used in an analogous manner referring to the pixel intensity or tonal distribution. Considering a pixel intensity histogram in which each bin is a measure of pixel intensity (tone), the bin value corresponds to a distance value within the standard meaning of COM. The bin count, or frequency, identifying the number of pixels having a particular intensity, or range of intensities, corresponds to a mass value within the standard meaning of COM.
  • the COM of a pixel intensity distribution provides a weighted average of the number of pixels having a given intensity value (i.e., bin) versus the value of the bin.
  • This analogy is useful in identifying that portion of the pixel intensity distribution in which a larger number of pixels of any given image can be found. It is this region having the proportionally larger number of pixels that can be moved toward a mid-point of the possible range of pixel intensities to improve the dynamic range of a given sample image.
  • the overexposure correction procedure i.e., the first sub-procedure
  • the overexposure correction procedure can be run again to ensure that the dynamic- range corrected image is not overexposed.
  • An image can be considered overexposed if a percentage of the pixels in the image have an intensity equal to the maximum resolvable intensity exceeds a predetermined threshold number, also known as the saturation limit.
  • This predetermined threshold number may be about 0.05 %, preferably, 0.02%, more preferably 0.0%.
  • a predetermined threshold number may be about 0.05 %, preferably, 0.02%, more preferably 0.0%.
  • E is the new exposure time
  • E ' is the currently set exposure time
  • A is an aggression level
  • SL is the saturation limit
  • CCD x and CCD y represent the pixel dimensions of the captured image
  • P is the count of pixels at maximum intensity.
  • the aggression level, A may vary but, generally, the values that one would want to choose would depend upon the amount by which images tend to be over saturated.
  • a value of zero (0) for A represents a minimum value for which the exposure time would be halved.
  • a practical maximum value for A is about 10, after which the exposure time will not change enough for the algorithm to be useful.
  • the value for A can fall in the range of about 0 ⁇ A ⁇ 4.5. More preferably, A is set at about 3.5.
  • the procedure of reducing exposure time to ensure the image is not overexposed is a multi-step process.
  • a 256 bin histogram is generated first for an 8-bit per pixel image obtained from the camera at the current exposure time, E'.
  • the number of saturated pixels are identified and compared to a predetermined saturation threshold value.
  • the over-exposure procedure is exited.
  • the exposure time is decreased.
  • the new, decreased exposure time can vary based upon the number of currently over exposed pixels.
  • a value S can be determined as
  • E ⁇ E'—0.5E' i.e., the exposure time would be halved.
  • the minimum amount of change to the current exposure time occurs when the number of over saturated pixels is very nearly equal to the saturation limit, in which case E ⁇ E'-0. OSSE' .
  • the procedure of reducing exposure time can be repeated in an iterative manner until the amount of overexposure is within a chosen threshold, or until a maximum number of iterations has been accomplished. In either instance the over-exposure correction routine is exited.
  • An alternative and equally viable process for correcting for overexposure is to acquire a new image at a minimum exposure time, then proceed with optimizing the exposure time by calculating the COM and bringing it within range of the midpoint, as described above.
  • the number of iterations x can fall in the range of about 5 ⁇ x ⁇ 150. In a preferred embodiment of the invention, the number of iterations x is set to fall in the range of about 10 ⁇ x ⁇ 30.
  • this step includes generation of a histogram of the image for intensity
  • a 256 bin histogram is generated of an 8 bit per pixel image from the camera at the current exposure time, E'. If the image is overexposed, the algorithm is exited.
  • the current center of mass, C, of the histogram can be determined as follows:
  • N is the number of histogram bins (256) and H 1 is the count of items in the i bin.
  • the new exposure time can be calculated by attempting to shift the current C to the center of the histogram.
  • the value of C can be compared to a center or midpoint of the pixel intensity distribution (histogram). If the current C falls after the midpoint (N/2) of the histogram, the adjusted exposure time can be determined as follows:
  • the adjusted exposure time can be determined as follows:
  • the above techniques can be automated, being performed by a processor, such as the controller 116 (FIG. 1), executing preprogrammed instructions to process a pixelized image of a sample, they can be executed much more quickly and reliably than if done manually by an operator.
  • the time savings is so substantial, that the AutoExposure process can be repeated for every FOV (histospot), while still completing analysis of a specimen within a reasonable time period.
  • the ability to perform AutoExposure on a FOV to FOV (histospot to histospot) basis is dependent upon the expression of all pixel values in an image in terms of a measure of signal power, instead of signal value.
  • the power P is obtained by diving the raw pixel value from the camera by the exposure time and the current bit-depth (i.e., 256 and 4096). This is an expression of the percent of maximum measurable intensity hitting a given pixel in the camera every millisecond and is given by
  • bit-depth is the bit-depth. Any bit-depth, such as 4096, can be used with no loss of generality.
  • the resulting value has units of ms " . Note that this is not a classical definition of power (energy per time) because the wavelength and frequency of light captured by the camera is not considered.
  • the overexposure correction process (e.g., steps 210 through 241 of FIG. 2) can be performed for an image without performing a COM analysis.
  • the COM process (e.g., step 210 and steps 250-271) can be performed for an image without performing an overexposure correction process.
  • both overexposure and COM analyses are performed for a given image, they may be performed in either order, with one or more of the individual procedures being repeated in any sequence as may be beneficial in analyzing a specimen.
  • the adjusted image can be further analyzed using any available image analysis techniques.
  • further analysis includes a quantitative analysis of the sample image, such as provided by the AQUA® analysis, yielding an AQUASCORE for a given sample image.
  • each tissue spot was qualitatively assessed for focus and gross staining artifacts, followed by automated validation for split-spot images, percent tumor areas (spots with less than the 5% tumor as assessed by total pixel count were redacted), % saturation (spots with greater that 5% saturation for DAPI, 4% saturation for Cy3, and 1% saturation for Cy5 were redacted), sum-channel (total) intensities (bottom 10% for both DAPI and Cy3 were redacted) and finally, the ratio of DAPI nuclear signal to DAPI signal in cytoplasm (spots less than 1.5 were redacted). It is important to note that as a critical endpoint assessment for Auto- Exposure, no tissue spot percentage saturation exceeded the maximum of 0.02% for either array.
  • FIG. 6A - FIG. 6D shows linear and non-parametric regression analysis of AQUA ® scores (FIG. 6A), DAPI exposure times (FIG. 6B), Cy3 exposure time (FIG. 6C), and Cy5 exposure times (FIG. 6D). All showed significant and robust correlation.

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Abstract

L'invention se rapporte à un système d'ajustement automatique d'un temps d'exposition pour améliorer ou sinon optimiser une plage dynamique d'une image numérique. Le système comprend un appareil de prise de vue configuré pour capturer une image d'un sujet à l'intérieur du champ de vision à un premier temps d'exposition. L'image capturée est composée de multiples pixels, chaque pixel ayant une valeur d'intensité respective. Le système comprend en outre un obturateur ou une commande appropriée configurée pour commander un temps d'exposition de l'appareil de prise de vue, et un contrôleur configuré pour exécuter les étapes suivantes comprenant : (a) la requête d'une distribution de fréquence de valeurs d'intensité de pixel ; (b) la détermination d'un « centre de masse » effectif de cette distribution, ou histogramme, pour déterminer un temps d'exposition ajusté ; et (c) la capture d'une seconde image du sujet au temps d'exposition ajusté, pour ainsi obtenir une image ayant une plage dynamique améliorée, ou optimale.
PCT/US2008/074817 2007-08-31 2008-08-29 Sélection de temps d'exposition automatique pour un tissu d'imagerie Ceased WO2009029810A1 (fr)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8120768B2 (en) 2007-06-15 2012-02-21 Historx, Inc. Method and system for standardizing microscope instruments
GB2499668A (en) * 2012-02-27 2013-08-28 Apical Ltd Exposure Controller
US8655037B2 (en) 2007-05-14 2014-02-18 Historx, Inc. Compartment segregation by pixel characterization using image data clustering
US9240043B2 (en) 2008-09-16 2016-01-19 Novartis Ag Reproducible quantification of biomarker expression
WO2016012761A1 (fr) * 2014-07-24 2016-01-28 King's College London Imagerie endoscopique
EP3565235A1 (fr) * 2018-05-04 2019-11-06 Guangdong Oppo Mobile Telecommunications Corp., Ltd Procédé et appareil de commande d'imagerie, dispositif d'imagerie et support d'informations lisible par ordinateur

Families Citing this family (40)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8055034B2 (en) * 2006-09-13 2011-11-08 Fluidigm Corporation Methods and systems for image processing of microfluidic devices
CA2596204C (fr) 2007-08-07 2019-02-26 Historx, Inc. Procede et systeme de determination de la dilution optimale d'un reactif
WO2009029810A1 (fr) * 2007-08-31 2009-03-05 Historx, Inc. Sélection de temps d'exposition automatique pour un tissu d'imagerie
US8675122B2 (en) * 2009-01-16 2014-03-18 Microsoft Corporation Determining exposure time in a digital camera
US8111905B2 (en) * 2009-10-29 2012-02-07 Mitutoyo Corporation Autofocus video tool and method for precise dimensional inspection
JP5356191B2 (ja) * 2009-11-26 2013-12-04 オリンパス株式会社 蛍光観察装置
RU2013124021A (ru) * 2010-10-25 2014-12-10 Конинклейке Филипс Электроникс Н.В. Система для сегментации медицинского изображения
US20130169788A1 (en) * 2010-10-29 2013-07-04 Canon Kabushiki Kaisha Microscope, image acquisition apparatus, and image acquisition system
US9161026B2 (en) * 2011-06-23 2015-10-13 Hewlett-Packard Development Company, L.P. Systems and methods for calibrating an imager
US10356336B2 (en) 2011-08-10 2019-07-16 National Ict Australia Limited Automatic exposure control for cameras
JP5784435B2 (ja) * 2011-09-20 2015-09-24 オリンパス株式会社 画像処理装置、蛍光顕微鏡装置および画像処理プログラム
EP3267206B1 (fr) 2012-03-30 2020-07-22 BD Kiestra B.V. Sélection automatique de microorganismes et identification à l'aide d'une dilam
US8446481B1 (en) 2012-09-11 2013-05-21 Google Inc. Interleaved capture for high dynamic range image acquisition and synthesis
US8866927B2 (en) 2012-12-13 2014-10-21 Google Inc. Determining an image capture payload burst structure based on a metering image capture sweep
US9087391B2 (en) 2012-12-13 2015-07-21 Google Inc. Determining an image capture payload burst structure
US8866928B2 (en) 2012-12-18 2014-10-21 Google Inc. Determining exposure times using split paxels
US9247152B2 (en) 2012-12-20 2016-01-26 Google Inc. Determining image alignment failure
US8995784B2 (en) 2013-01-17 2015-03-31 Google Inc. Structure descriptors for image processing
EP2951543B1 (fr) * 2013-01-31 2022-05-25 Ventana Medical Systems, Inc. Systèmes et procédés pour étalonner, configurer et valider un dispositif ou un système d'imagerie pour dosages tissulaires multiplex
US9686537B2 (en) 2013-02-05 2017-06-20 Google Inc. Noise models for image processing
US9117134B1 (en) 2013-03-19 2015-08-25 Google Inc. Image merging with blending
US9066017B2 (en) 2013-03-25 2015-06-23 Google Inc. Viewfinder display based on metering images
US9131201B1 (en) 2013-05-24 2015-09-08 Google Inc. Color correcting virtual long exposures with true long exposures
US9077913B2 (en) 2013-05-24 2015-07-07 Google Inc. Simulating high dynamic range imaging with virtual long-exposure images
KR20150017617A (ko) * 2013-08-07 2015-02-17 삼성전자주식회사 광간섭 단층 영상의 처리 방법 및 이를 수행하기 위한 장치
US9615012B2 (en) * 2013-09-30 2017-04-04 Google Inc. Using a second camera to adjust settings of first camera
CN111263076B (zh) * 2014-01-30 2021-06-18 Bd科斯特公司 用于使用监督式高品质成像的图像采集的系统和方法
EP2913993B1 (fr) * 2014-02-26 2016-02-17 Axis AB Procédé et unité de commande pour commander l'exposition d'une caméra
US9380224B2 (en) * 2014-02-28 2016-06-28 Microsoft Technology Licensing, Llc Depth sensing using an infrared camera
US9436870B1 (en) * 2014-06-06 2016-09-06 Amazon Technologies, Inc. Automatic camera selection for head tracking using exposure control
US9729782B2 (en) * 2015-06-05 2017-08-08 Digital Signal Corporation System and method for intelligent camera control
CN109496187B (zh) * 2016-08-08 2022-07-26 金泰克斯公司 用于处理视频数据以通过动态曝光控制来检测和消除闪变光源的系统和方法
US10417476B2 (en) * 2016-12-30 2019-09-17 Eosmem Corporation Exposure time determination method for image sensing operation
US10778910B2 (en) * 2017-03-22 2020-09-15 Humaneyes Technologies Ltd. System and methods for correcting overlapping digital images of a panorama
EP3644044B1 (fr) * 2018-10-24 2020-12-23 Leica Biosystems Imaging, Inc. Commande d'exposition de caméra lors de l'acquisition des images d'hybridation in situ en fluorescence
US11257237B2 (en) 2019-08-29 2022-02-22 Microsoft Technology Licensing, Llc Optimized exposure control for improved depth mapping
EP4092466A1 (fr) * 2021-05-21 2022-11-23 Leica Microsystems CMS GmbH Procédé d'obtention d'une valeur de fonctionnement d'un paramètre d'éclairage pour examiner un échantillon fluorescent, procédé d'examen d'un échantillon fluorescent, système de microscope et programme informatique
CN115883972A (zh) * 2022-11-28 2023-03-31 中国人民解放军战略支援部队航天工程大学 一种基于自动曝光的高轨目标光学探测方法及系统
JP2025004459A (ja) * 2023-06-26 2025-01-15 キヤノン株式会社 光電変換装置、画像処理装置、移動体、処理方法、及びコンピュータプログラム
CN119450216B (zh) * 2024-11-12 2025-10-28 中国科学院合肥物质科学研究院 一种差分吸收光谱仪的自动曝光方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000064147A1 (fr) * 1999-04-20 2000-10-26 Intel Corporation Determination automatique du reglage final de l'exposition pour une camera a semi-conducteurs sans circuit de posemetre separe
EP1202563A1 (fr) * 2000-10-26 2002-05-02 Olympus Optical Co., Ltd Appareil de prise d'images
WO2002056584A1 (fr) * 2001-01-11 2002-07-18 Smal Camera Technologies Commande de sensibilite adptative, a base pixel par pixel, destinee a un systeme de production d'images numerique
US20020141049A1 (en) * 2001-03-22 2002-10-03 Olympus Optical Co., Ltd. Imaging apparatus for microscope
US20040085475A1 (en) * 2002-10-31 2004-05-06 Motorola, Inc. Automatic exposure control system for a digital camera
JP2005070537A (ja) * 2003-08-26 2005-03-17 Olympus Corp 顕微鏡像撮像装置及び顕微鏡像撮像方法

Family Cites Families (150)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE3037622C2 (de) * 1980-10-04 1987-02-26 Theodor Prof. Dr.-Ing. 1000 Berlin Gast Einrichtung zur Bestimmung der Oberflächengüte
DE3429541C2 (de) * 1984-08-10 1992-10-22 Seidenberg, Jürgen, Dipl.-Ing., 5100 Aachen Verfahren zur gleichzeitigen Bestimmung der Wellenlänge und der Strahlungsleistung einer monochromatischen Lichtquelle
US4998284A (en) 1987-11-17 1991-03-05 Cell Analysis Systems, Inc. Dual color camera microscope and methodology for cell staining and analysis
US4892817A (en) * 1987-09-21 1990-01-09 Biogenex Laboratories Stable phosphatase substrate composition
US4912034A (en) * 1987-09-21 1990-03-27 Biogenex Laboratories Immunoassay test device and method
JPH0663758B2 (ja) * 1987-10-14 1994-08-22 株式会社東芝 パターンの測定方法
DE3800427C2 (de) * 1988-01-09 1997-05-28 Breitmeier Ulrich Gerät zum Ermitteln des Abstands eines auf einer Prüffläche liegenden Prüfpunktes von einer Referenzfläche
US5068909A (en) 1989-05-18 1991-11-26 Applied Imaging Corporation Method and apparatus for generating quantifiable video displays
US5244787A (en) * 1991-01-31 1993-09-14 Biogenex Laboratories Antigen retrieval in formalin-fixed tissues using microwave energy
US6411377B1 (en) 1991-04-02 2002-06-25 Hitachi, Ltd. Optical apparatus for defect and particle size inspection
US5126577A (en) * 1991-06-27 1992-06-30 Electro-Optical Industries, Inc. Infrared target plate handling apparatus with improved thermal control
CA2084099C (fr) 1991-12-06 1996-06-18 James V. Bacus Methode et appareil d'analyse cellulaire automatisee
US5578452A (en) 1992-01-16 1996-11-26 Biogenex Laboratories Enhancement of immunochemical staining in aldehyde-fixed tissues
US5254845A (en) 1992-08-18 1993-10-19 Applied Imaging International Limited Automatic focusing system using focal length-changing element
US5889881A (en) * 1992-10-14 1999-03-30 Oncometrics Imaging Corp. Method and apparatus for automatically detecting malignancy-associated changes
US6026174A (en) * 1992-10-14 2000-02-15 Accumed International, Inc. System and method for automatically detecting malignant cells and cells having malignancy-associated changes
US5427910A (en) * 1992-12-09 1995-06-27 Compucyte Corporation Method of cytogenetic analysis
US5587833A (en) 1993-07-09 1996-12-24 Compucyte Corporation Computerized microscope specimen encoder
US5492837A (en) * 1993-08-27 1996-02-20 Biogenex Laboratories Mounting medium for microscope slide preparations
US5439649A (en) * 1993-09-29 1995-08-08 Biogenex Laboratories Automated staining apparatus
US6136540A (en) * 1994-10-03 2000-10-24 Ikonisys Inc. Automated fluorescence in situ hybridization detection of genetic abnormalities
US5432054A (en) * 1994-01-31 1995-07-11 Applied Imaging Method for separating rare cells from a population of cells
US5489386A (en) * 1994-01-31 1996-02-06 Applied Imaging Density gradient medium for the separation of cells
US5422018A (en) * 1994-01-31 1995-06-06 Applied Imaging Centrifuge tube and adaptor
US6632598B1 (en) * 1994-03-11 2003-10-14 Biogenex Laboratories Deparaffinization compositions and methods for their use
US6451551B1 (en) 1994-03-11 2002-09-17 Biogenex Laboratories Releasing embedding media from tissue specimens
US5561556A (en) 1994-04-21 1996-10-01 Compucyte Corporation Slide analysis system with slide having self contained microscope analysis information
US5978497A (en) 1994-09-20 1999-11-02 Neopath, Inc. Apparatus for the identification of free-lying cells
DE19532897A1 (de) 1994-09-27 1996-03-28 Zeiss Carl Fa Verfahren zur Belichtungssteuerung bei Mikroskopkameras und entsprechendes Photomikroskop
US5633945A (en) * 1994-10-05 1997-05-27 Compucyte Corporation Accuracy in cell mitosis analysis
US5523207A (en) * 1994-10-17 1996-06-04 Compucyte Corporation Method for accurate counting of probe spots in cell nuclei
DE69519980T2 (de) 1994-12-28 2001-06-07 Siemens Corp. Research, Inc. Verfahren und Gerät zur Detektion und Interpretation von Untertiteln in digitalen Videosignalen
US5694212A (en) 1995-06-20 1997-12-02 Compucyte Corporation Method for calibrating specimen with specimen holder of a microscope
US5717198A (en) * 1995-07-10 1998-02-10 Qc Optics, Inc. Pellicle reflectivity monitoring system having means for compensating for portions of light reflected by the pellicle
CA2185511C (fr) 1995-09-15 2008-08-05 Vladimir Dadeshidze Systeme d'analyse d'un specimen cytologique avec les donnees individuelles pour chaque patient
US6718053B1 (en) * 1996-11-27 2004-04-06 Chromavision Medical Systems, Inc. Method and apparatus for automated image analysis of biological specimens
US6418236B1 (en) * 1999-06-24 2002-07-09 Chromavision Medical Systems, Inc. Histological reconstruction and automated image analysis
US6151405A (en) 1996-11-27 2000-11-21 Chromavision Medical Systems, Inc. System and method for cellular specimen grading
US6330349B1 (en) 1995-11-30 2001-12-11 Chromavision Medical Systems, Inc. Automated method for image analysis of residual protein
ATE236386T1 (de) * 1995-11-30 2003-04-15 Chromavision Med Sys Inc Verfahren zur automatischen bildanalyse biologischer proben
US6239868B1 (en) * 1996-01-02 2001-05-29 Lj Laboratories, L.L.C. Apparatus and method for measuring optical characteristics of an object
US6882873B2 (en) * 1996-01-17 2005-04-19 Respironics, Inc. Method and system for determining bilirubin concentration
US6165739A (en) 1996-03-13 2000-12-26 Compucyte Corporation Multiple simultaneous testing media
DE69725611T2 (de) 1996-05-15 2004-07-29 Biogenex Laboratories, San Ramon Nicht-Nukleotid verknüpfende Reagentien
US6031930A (en) * 1996-08-23 2000-02-29 Bacus Research Laboratories, Inc. Method and apparatus for testing a progression of neoplasia including cancer chemoprevention testing
US6404906B2 (en) * 1997-03-03 2002-06-11 Bacus Research Laboratories,Inc. Method and apparatus for acquiring and reconstructing magnified specimen images from a computer-controlled microscope
US6272235B1 (en) * 1997-03-03 2001-08-07 Bacus Research Laboratories, Inc. Method and apparatus for creating a virtual microscope slide
US6396941B1 (en) * 1996-08-23 2002-05-28 Bacus Research Laboratories, Inc. Method and apparatus for internet, intranet, and local viewing of virtual microscope slides
US5731156A (en) * 1996-10-21 1998-03-24 Applied Imaging, Inc. Use of anti-embryonic hemoglobin antibodies to identify fetal cells
US5880473A (en) * 1997-07-28 1999-03-09 Applied Imaging, Inc. Multifluor-fluorescence in-situ hybridization (M-FISH) imaging techniques using multiple multiband filters with image registration
AU4920297A (en) * 1996-10-25 1998-05-15 Applied Imaging, Inc. Multifluor-fluorescence in situ hybridization (m-fish) imaging techniques using multiple multiband filters with image registration
US5916750A (en) * 1997-01-08 1999-06-29 Biogenex Laboratories Multifunctional linking reagents for synthesis of branched oligomers
US6087134A (en) 1997-01-14 2000-07-11 Applied Imaging Corporation Method for analyzing DNA from a rare cell in a cell population
US5885840A (en) * 1997-02-10 1999-03-23 Compucyte Corp. Multiple assays of cell specimens
GB9704054D0 (en) * 1997-02-27 1997-04-16 Univ Cambridge Tech Assay
US5948359A (en) 1997-03-21 1999-09-07 Biogenex Laboratories Automated staining apparatus
US6169816B1 (en) * 1997-05-14 2001-01-02 Applied Imaging, Inc. Identification of objects of interest using multiple illumination schemes and finding overlap of features in corresponding multiple images
US6259807B1 (en) 1997-05-14 2001-07-10 Applied Imaging Corp. Identification of objects of interest using multiple illumination schemes and finding overlap of features in corresponding multiple images
US6052190A (en) * 1997-09-09 2000-04-18 Utoptics, Inc. Highly accurate three-dimensional surface digitizing system and methods
US6002788A (en) 1997-09-24 1999-12-14 Compucyte Corporation Method and device for rendering cells observable with laser scanning cytometry
US5962234A (en) 1997-10-20 1999-10-05 Applied Imaging Corporation Use of anti-embryonic epsilon hemoglobin antibodies to identify fetal cells
US6746873B1 (en) * 1998-02-20 2004-06-08 Xy, Inc. Vibratory system for a sorting flow cytometer
US6699710B1 (en) * 1998-02-25 2004-03-02 The United States Of America As Represented By The Department Of Health And Human Services Tumor tissue microarrays for rapid molecular profiling
US6248590B1 (en) * 1998-02-27 2001-06-19 Cytomation, Inc. Method and apparatus for flow cytometry
US6495106B1 (en) 1998-03-24 2002-12-17 Biogenex Laboratories Automated staining apparatus
US6606413B1 (en) * 1998-06-01 2003-08-12 Trestle Acquisition Corp. Compression packaged image transmission for telemicroscopy
ATE536422T1 (de) 1998-08-25 2011-12-15 Univ Washington Schnelle quantitative analyse von proteinen oder proteinfunktionen in komplexen gemischen
DE19915057A1 (de) * 1999-04-01 2000-10-19 Forschungszentrum Borstel Monoklonale Antikörper gegen das humane Mcm3 Protein, Verfahren zu ihrer Herstellung und ihre Verwendung
CA2366524A1 (fr) 1999-04-13 2000-10-19 Chromavision Medical Systems, Inc. Reconstruction histologique et analyse d'images automatisee
US6876760B1 (en) * 2000-12-04 2005-04-05 Cytokinetics, Inc. Classifying cells based on information contained in cell images
AU5606500A (en) * 1999-06-18 2001-01-09 Applied Imaging Corporation High efficiency methods for combined immunocytochemistry and in-situ hybridization
US6453060B1 (en) 1999-06-29 2002-09-17 Tri Path Imaging, Inc. Method and apparatus for deriving separate images from multiple chromogens in a branched image analysis system
WO2001015452A1 (fr) * 1999-07-13 2001-03-01 Chromavision Medical Systems, Inc. Dispositif permettant d'appliquer en temps reel des seuils de couleurs exacts
US6445817B1 (en) 1999-07-13 2002-09-03 Chromavision Medical Systems, Inc. Apparatus for counting color transitions and areas in real time camera images
JP3766803B2 (ja) * 1999-08-04 2006-04-19 クロマビジョン メディカル システムズ インコーポレイテッド 光学顕微鏡使用における色彩閾値を与える方法およびその装置
US7129053B1 (en) 1999-10-12 2006-10-31 Dakocytomation Denmark A/S Immuno-chromatographic rapid assay in order to detect acid-resistant microorganisms in the stool
US7024316B1 (en) * 1999-10-21 2006-04-04 Dakocytomation Colorado, Inc. Transiently dynamic flow cytometer analysis system
JP2001211896A (ja) 1999-11-26 2001-08-07 Olympus Optical Co Ltd 画像解析方法、装置、及び記録媒体
GB0001568D0 (en) * 2000-01-24 2000-03-15 Isis Innovation Method and apparatus for measuring surface configuration
US7236623B2 (en) * 2000-04-24 2007-06-26 International Remote Imaging Systems, Inc. Analyte recognition for urinalysis diagnostic system
US6518554B1 (en) * 2000-05-24 2003-02-11 Chromavision Medical Systems, Inc. Reverse focusing methods and systems
US6522744B1 (en) * 2000-05-31 2003-02-18 Jung-Hsun Chiang Volume control apparatus of telephone
CN1451230A (zh) * 2000-07-21 2003-10-22 纽约市哥伦比亚大学托管会 图象拼合方法与设备
US6674058B1 (en) * 2000-09-20 2004-01-06 Compucyte Corporation Apparatus and method for focusing a laser scanning cytometer
US7262799B2 (en) * 2000-10-25 2007-08-28 Canon Kabushiki Kaisha Image sensing apparatus and its control method, control program, and storage medium
GB2406908B (en) 2000-12-04 2005-07-27 Cytokinetics Inc Classifying cells based on information contained in cell images
US20030215936A1 (en) 2000-12-13 2003-11-20 Olli Kallioniemi High-throughput tissue microarray technology and applications
US6466690C1 (en) 2000-12-19 2008-11-18 Bacus Res Lab Inc Method and apparatus for processing an image of a tissue sample microarray
US6993169B2 (en) * 2001-01-11 2006-01-31 Trestle Corporation System and method for finding regions of interest for microscopic digital montage imaging
DE10109130B4 (de) 2001-02-24 2015-02-19 Carl Zeiss Microscopy Gmbh Verfahren zur Aufzeichnung und Darstellung von Fluoreszenzbildern mit hoher räumlicher Auflösung
US7146062B2 (en) 2001-03-08 2006-12-05 Clarient, Inc. Apparatus and method for labeling rows and columns in an irregular array
DE10119005A1 (de) 2001-04-18 2002-10-24 Roche Diagnostics Gmbh Verfahren zur Proteinexpression ausgehend von stabilisierter linearer kurzer DNA in zellfreien in vitro-Transkription/Translations-Systemen mit Exonuklease-haltigen Lysaten oder in einem zellulären System enthaltend Exonukleasen
US7219016B2 (en) 2001-04-20 2007-05-15 Yale University Systems and methods for automated analysis of cells and tissues
WO2002098280A2 (fr) 2001-06-04 2002-12-12 Ikonisys Inc. Procede de detection d'agents infectieux au moyen d'une analyse automatisee geree par ordinateur
EP1393074A1 (fr) 2001-06-05 2004-03-03 Genomics, Ltd. Compositions et procedes pour la detection des biomarqueurs associes a des maladies cardio-vasculaires
US7133543B2 (en) 2001-06-12 2006-11-07 Applied Imaging Corporation Automated scanning method for pathology samples
US7229774B2 (en) * 2001-08-02 2007-06-12 Regents Of The University Of Michigan Expression profile of prostate cancer
US7332290B2 (en) * 2001-08-02 2008-02-19 The Regents Of The University Of Michigan Dectection of AMACR cancer markers in urine
US6697509B2 (en) * 2001-10-04 2004-02-24 Chromavision Medical Systems, Inc. Method and apparatus for scoring the uptake of markers in cells
US7383134B2 (en) * 2002-01-15 2008-06-03 Piper James R Method and/or system for analyzing biological samples using a computer system
EP1468266A4 (fr) * 2002-01-22 2009-03-11 Beckman Coulter Inc Systeme de confinement d'environnement pour un cytometre de flux
JP2003284713A (ja) 2002-03-28 2003-10-07 Konica Corp 医用画像処理装置、画像処理パラメータの補正方法、プログラム、記憶媒体
JP2003294734A (ja) 2002-03-29 2003-10-15 Mitsubishi Chemicals Corp 分子間の相互作用の分析方法
US7376256B2 (en) * 2002-04-16 2008-05-20 Evotec Oai Ag Method for analyzing chemical and or biological samples by means of particle images
WO2003093810A1 (fr) * 2002-05-03 2003-11-13 Vialogy Corporation Systeme et procede de caracterisation de donnees de sortie d'un microreseau
US7272252B2 (en) 2002-06-12 2007-09-18 Clarient, Inc. Automated system for combining bright field and fluorescent microscopy
US20050037406A1 (en) * 2002-06-12 2005-02-17 De La Torre-Bueno Jose Methods and apparatus for analysis of a biological specimen
WO2003106157A2 (fr) 2002-06-14 2003-12-24 Chromavision Medical Systems, Inc. Appareil automatique de coloration des lames
GB2395263A (en) 2002-11-12 2004-05-19 Qinetiq Ltd Image analysis
GB2396406A (en) 2002-12-17 2004-06-23 Qinetiq Ltd Image analysis
US7062079B2 (en) * 2003-02-14 2006-06-13 Ikonisys, Inc. Method and system for image segmentation
US7233340B2 (en) * 2003-02-27 2007-06-19 Applied Imaging Corp. Linking of images to enable simultaneous viewing of multiple objects
US7064829B2 (en) * 2003-03-20 2006-06-20 Timbre Technologies, Inc. Generic interface for an optical metrology system
US7369696B2 (en) * 2003-04-02 2008-05-06 Ge Healthcare Uk Limited Classification of cells into subpopulations using cell classifying data
US7171054B2 (en) * 2003-05-01 2007-01-30 Eastman Kodak Company Scene-based method for determining focus
US7084386B2 (en) * 2003-05-02 2006-08-01 International Business Machines Corporation System and method for light source calibration
JP2004354346A (ja) 2003-05-30 2004-12-16 Olympus Corp 測定装置
FI116246B (fi) * 2003-06-30 2005-10-14 Nokia Corp Menetelmä ja järjestelmä digitaalisessa kuvannuksessa valotuksen säätämiseksi ja vastaava laite
US8068988B2 (en) * 2003-09-08 2011-11-29 Ventana Medical Systems, Inc. Method for automated processing of digital images of tissue micro-arrays (TMA)
WO2005055925A2 (fr) * 2003-09-10 2005-06-23 Board Of Regents, The University Of Texas System Surexpression de 14-3-3 zeta en tant que facteur de pronostic sombre, et cible therapeutique dans plusieurs types de cancer
WO2005027015A2 (fr) * 2003-09-10 2005-03-24 Bioimagene, Inc. Procede et systeme d'analyse quantitative d'echantillons biologiques
US7760927B2 (en) * 2003-09-10 2010-07-20 Bioimagene, Inc. Method and system for digital image based tissue independent simultaneous nucleus cytoplasm and membrane quantitation
US20050282179A1 (en) 2003-12-05 2005-12-22 Applera Corporation Biomarkers for interferon-alpha response in hepatitis C virus infected patients
WO2006017811A2 (fr) 2004-08-06 2006-02-16 Compucyte Corporation Absorption de lumiere monochromatique a couleurs multiples et quantification d'absorption de lumiere dans un echantillon colore
AU2005289756B2 (en) * 2004-09-22 2011-10-13 Tripath Imaging, Inc. Methods and computer program products for analysis and optimization of marker candidates for cancer prognosis
US20060160169A1 (en) * 2004-12-03 2006-07-20 Board Of Regents, The University Of Texas System Cell microarray for profiling of cellular phenotypes and gene function
JP2006194711A (ja) 2005-01-13 2006-07-27 Matsushita Electric Ind Co Ltd 蛍光発光色素の性能評価方法および性能評価装置
EP1844426A4 (fr) 2005-02-01 2016-09-07 Amnis Corp Analyse sanguine et cellulaire au moyen d'un cytometre en flux imageur
GB0503087D0 (en) 2005-02-15 2005-03-23 Medical Solutions Plc Apparatus and method for processing of specimen images for use in computer analysis thereof
JP4315119B2 (ja) 2005-03-29 2009-08-19 セイコーエプソン株式会社 液滴吐出装置、パターン形成方法、及び電気光学装置の製造方法
US20060275844A1 (en) 2005-04-19 2006-12-07 Linke Steven P Diagnostic markers of breast cancer treatment and progression and methods of use thereof
BRPI0501535B8 (pt) * 2005-04-26 2021-07-27 Mario Augusto Pazoti método de identificação do guignardia citricarpa
WO2006133325A2 (fr) 2005-06-08 2006-12-14 Vanderbilt University Analyse d'echantillons de tissus entourant des tumeurs malignes
WO2006133460A2 (fr) 2005-06-09 2006-12-14 Yale University Methodes de diagnostic et de traitement du cancer du sein d'apres le rapport her/er
GB2430026A (en) 2005-09-09 2007-03-14 Qinetiq Ltd Automated selection of image regions
PT1934607E (pt) * 2005-10-13 2013-11-13 Fundacao D Anna Sommer Champalimaud E Dr Carlos Montez Champalimaud Análise imuno-histoquímica in situ multiplex
JP4863692B2 (ja) * 2005-11-02 2012-01-25 株式会社島津製作所 イメージ質量分析装置
DE602007009645D1 (de) 2006-02-16 2010-11-18 Ventana Med Syst Inc Reagenzien und verfahren für krebsprognose und pathologische einstufung
US20080118437A1 (en) * 2006-03-01 2008-05-22 Regents Of The University Of Michigan Diagnosis and treatment O prostate cancer
JP4748719B2 (ja) 2006-03-02 2011-08-17 国立大学法人大阪大学 生細胞内の特定タンパク質の定量方法および標準蛍光マイクロビーズの作製方法
JP2007271484A (ja) 2006-03-31 2007-10-18 Toyobo Co Ltd 細胞成分分析装置の感度を向上する方法
JP4864519B2 (ja) 2006-04-11 2012-02-01 浜松ホトニクス株式会社 光測定装置、光測定方法、及び光測定プログラム
WO2008008500A2 (fr) * 2006-07-13 2008-01-17 Yale University Procédés pronostiques du cancer à partir de la localisation subcellulaire de biomarqueurs
CA2674084C (fr) * 2006-12-26 2013-05-14 Pharmacyclics, Inc. Procede d'utilisation des inhibiteurs de l'histone desacetylase et de suivi des biomarqueurs dans une therapie combinee
CA2690612C (fr) 2007-05-14 2016-07-05 Historx, Inc. Separation en compartiments par caracterisation de pixel utilisant le regroupement de donnees d'image
US7907271B2 (en) 2007-06-15 2011-03-15 Historx, Inc. Method and system for standardizing microscope instruments
CA2604317C (fr) 2007-08-06 2017-02-28 Historx, Inc. Methodes et systeme permettant de valider des echantillons d'images pour dosages immunologiques quantitatifs
CA2596204C (fr) 2007-08-07 2019-02-26 Historx, Inc. Procede et systeme de determination de la dilution optimale d'un reactif
WO2009029810A1 (fr) 2007-08-31 2009-03-05 Historx, Inc. Sélection de temps d'exposition automatique pour un tissu d'imagerie
WO2010033508A1 (fr) 2008-09-16 2010-03-25 Historx, Inc. Quantification reproductible de l'expression de biomarqueurs

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000064147A1 (fr) * 1999-04-20 2000-10-26 Intel Corporation Determination automatique du reglage final de l'exposition pour une camera a semi-conducteurs sans circuit de posemetre separe
EP1202563A1 (fr) * 2000-10-26 2002-05-02 Olympus Optical Co., Ltd Appareil de prise d'images
WO2002056584A1 (fr) * 2001-01-11 2002-07-18 Smal Camera Technologies Commande de sensibilite adptative, a base pixel par pixel, destinee a un systeme de production d'images numerique
US20020141049A1 (en) * 2001-03-22 2002-10-03 Olympus Optical Co., Ltd. Imaging apparatus for microscope
US20040085475A1 (en) * 2002-10-31 2004-05-06 Motorola, Inc. Automatic exposure control system for a digital camera
JP2005070537A (ja) * 2003-08-26 2005-03-17 Olympus Corp 顕微鏡像撮像装置及び顕微鏡像撮像方法

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8655037B2 (en) 2007-05-14 2014-02-18 Historx, Inc. Compartment segregation by pixel characterization using image data clustering
US8120768B2 (en) 2007-06-15 2012-02-21 Historx, Inc. Method and system for standardizing microscope instruments
US9240043B2 (en) 2008-09-16 2016-01-19 Novartis Ag Reproducible quantification of biomarker expression
GB2499668A (en) * 2012-02-27 2013-08-28 Apical Ltd Exposure Controller
US9584732B2 (en) 2012-02-27 2017-02-28 Apical, Ltd. Exposure controller
GB2499668B (en) * 2012-02-27 2019-03-06 Apical Ltd Exposure controller
WO2016012761A1 (fr) * 2014-07-24 2016-01-28 King's College London Imagerie endoscopique
US10609291B2 (en) 2014-07-24 2020-03-31 King's College London Automatic exposure control for endoscopic imaging
EP3565235A1 (fr) * 2018-05-04 2019-11-06 Guangdong Oppo Mobile Telecommunications Corp., Ltd Procédé et appareil de commande d'imagerie, dispositif d'imagerie et support d'informations lisible par ordinateur
US10728473B2 (en) 2018-05-04 2020-07-28 Guangdong Oppo Mobile Telecommunications Corp., Ltd. Imaging control method, imaging device, and computer-readable storage medium

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